In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.
When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.
Let us begin by defining the two terms.
Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.
Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.
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